eess.AS · 2026-06-10 · No. 19

Audio and Speech Processing, 2026-06-10.

2 new papers in eess.AS. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

2 entries
  1. 01

    Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks

    Ipek Sen, Ozgur Ozdemir, Elena Battini Sonmez

    eess.AS · cs.AI

    This study aims to explore the performance of the VAR model in comparison with mel-frequency cepstral coefficient (MFCC) matrices and log-mel spectrograms using deep learning. In pulmonary sound classification, spectrogram-based representations suffer from inconsistent temporal dimensions due to varying respiratory cycle durations. Along with traditional trimming/zero-padding, adaptive-length windowing was presented to fix their temporal...

    arxiv.org/abs/2606.10972 · PDF

  2. 02

    Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding

    Zhiyuan Zhu, Yixuan Chen, Yiwen Shao, Wenxiang Guo, Changhao Pan, Yu Zhang, Yuxiang Wang, Wei Liu, Houhua Zhang,...

    eess.AS · cs.AI

    Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for sound localization, spatial relation reasoning, and spatial scene understanding. We propose Spatial-Omni, a lightweight method that implements SO-Encoder to inject First-Order Ambisonics (FOA) spatial audio into existing Omni LLMs as an independent modality, without modifying their original audio...

    arxiv.org/abs/2606.10738 · PDF

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